Valuation of Specific Crime Rates in the United States, 1980 and 1990 (ICPSR 3161)

This project was designed to isolate the effects that
individual crimes have on wage rates and housing prices, as gauged by
individuals' and households' decisionmaking preferences changing over
time. Additionally, this project sought to compute a dollar value
that individuals would bear in their wages and housing costs to reduce
the rates of specific crimes. The study used multiple decades of
information obtained from counties across the United States to create
a panel dataset. This approach was designed to compensate for the
problem of collinearity by tracking how housing and occupation choices
within particular locations changed over the decade considering
all amenities or disamenities, including specific crime rates. Census
data were obtained for this project from the Integrated Public Use
Microdata Series (IPUMS) constructed by Ruggles and Sobek
(1997). Crime data were obtained from the Federal Bureau of
Investigation's Uniform Crime Reports (UCR). Other data were collected
from the American Chamber of Commerce Researchers Association, County
and City Data Book, National Oceanic and Atmospheric Administration,
and Environmental Protection Agency. Independent variables for the
Wages Data (Part 1) include years of education, school enrollment,
sex, ability to speak English well, race, veteran status, employment
status, and occupation and industry. Independent variables for the
Housing Data (Part 2) include number of bedrooms, number of other
rooms, building age, whether unit was a condominium or detached
single-family house, acreage, and whether the unit had a kitchen,
plumbing, public sewers, and water service. Both files include the
following variables as separating factors: census geographic division,
cost-of-living index, percentage unemployed, percentage vacant
housing, labor force employed in manufacturing, living near a
coastline, living or working in the central city, per capita local
taxes, per capita intergovernmental revenue, per capita property
taxes, population density, and commute time to work. Lastly, the
following variables measured amenities or disamenities: average
precipitation, temperature, windspeed, sunshine, humidity,
teacher-pupil ratio, number of Superfund sites, total suspended
particulate in air, and rates of murder, rape, robbery, aggravated
assault, burglary, larceny, auto theft, violent crimes, and property
crimes.

This project was designed to isolate the effects that
individual crimes have on wage rates and housing prices, as gauged by
individuals' and households' decisionmaking preferences changing over
time. Additionally, this project sought to compute a dollar value
that individuals would bear in their wages and housing costs to reduce
the rates of specific crimes. The study used multiple decades of
information obtained from counties across the United States to create
a panel dataset. This approach was designed to compensate for the
problem of collinearity by tracking how housing and occupation choices
within particular locations changed over the decade considering
all amenities or disamenities, including specific crime rates. Census
data were obtained for this project from the Integrated Public Use
Microdata Series (IPUMS) constructed by Ruggles and Sobek
(1997). Crime data were obtained from the Federal Bureau of
Investigation's Uniform Crime Reports (UCR). Other data were collected
from the American Chamber of Commerce Researchers Association, County
and City Data Book, National Oceanic and Atmospheric Administration,
and Environmental Protection Agency. Independent variables for the
Wages Data (Part 1) include years of education, school enrollment,
sex, ability to speak English well, race, veteran status, employment
status, and occupation and industry. Independent variables for the
Housing Data (Part 2) include number of bedrooms, number of other
rooms, building age, whether unit was a condominium or detached
single-family house, acreage, and whether the unit had a kitchen,
plumbing, public sewers, and water service. Both files include the
following variables as separating factors: census geographic division,
cost-of-living index, percentage unemployed, percentage vacant
housing, labor force employed in manufacturing, living near a
coastline, living or working in the central city, per capita local
taxes, per capita intergovernmental revenue, per capita property
taxes, population density, and commute time to work. Lastly, the
following variables measured amenities or disamenities: average
precipitation, temperature, windspeed, sunshine, humidity,
teacher-pupil ratio, number of Superfund sites, total suspended
particulate in air, and rates of murder, rape, robbery, aggravated
assault, burglary, larceny, auto theft, violent crimes, and property
crimes.

Access Notes

The public-use data files in this collection are available for access by the general public.
Access does not require affiliation with an ICPSR member institution.

Study Description

Citation

Bartley, William Alan. VALUATION OF SPECIFIC CRIME RATES IN THE UNITED STATES, 1980 AND 1990. ICPSR version. Nashville, TN: Vanderbilt University [producer], 2001. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2001. https://doi.org/10.3886/ICPSR03161.v1

Universe:
All individuals and households in the United States in
1980 or 1990.

Data Type(s):
census/enumeration data, and aggregate data

Data Collection Notes:

The user guide and codebook are provided by ICPSR as
Portable Document Format (PDF) files. The PDF file format was
developed by Adobe Systems Incorporated and can be accessed using PDF
reader software, such as the Adobe Acrobat Reader. Information on how
to obtain a copy of the Acrobat Reader is provided on the ICPSR Web
site.

Methodology

Study Purpose:
This study aimed to evaluate the explicit costs
of crime to society. There have been two previous approaches to this
problem. First, hedonic models sought to isolate the value individuals
placed on specific amenities or disamenities, such as weather, air
pollution, and crime rates, as seen in the wages they required and the
prices they paid for housing. The second approach evaluated costs by
combining the actual out-of-pocket expenses associated with crime with
the imputed costs from the pain, suffering, and fear of crime. This
data collection combined these two methods to obtain a market-based
estimate for specific crimes. In particular, this project was designed
to isolate the effects that individual crimes have on wage rates (Part
1) and housing prices (Part 2), as gauged by individuals' and
households' decisionmaking preferences changing over time.
Additionally this project sought to compute a dollar value that
individuals would bear in their wages and housing costs to reduce the
rates of specific crimes.

Study Design:
The study used multiple decades of information
obtained from counties across the United States to create a panel
dataset. This approach was designed to compensate for the problem of
collinearity by tracking how housing and occupation choices within
particular locations changed over the decade considering all
amenities or disamenities, including specific crime rates. Census data
were obtained for this project from the Integrated Public Use
Microdata Series (IPUMS) constructed by Ruggles and Sobek (1997). To
improve upon previous research, this data collection utilized Sample B
Census data, which include information from more urban areas and also
include urban areas that cross state lines. Crime data were obtained
from the Federal Bureau of Investigation's Uniform Crime Reports
(UCR). Other data were collected from the American Chamber of Commerce
Researchers Association, County and City Data Book, National Oceanic
and Atmospheric Administration, and Environmental Protection Agency.

Sample:
Nationally representative sample.

Data Source:

Data were obtained from the Integrated Public Use
Microdata Series (IPUMS), the Uniform Crime Reports (UCR) from the
Federal Bureau of Investigation, the American Chamber of Commerce
Researchers Association (ACCRA), the County and City Data Book, the
National Oceanic and Atmospheric Administration, and the Environmental
Protection Agency.

Description of Variables:
Independent variables for the Wages Data (Part 1)
include years of education, school enrollment, sex, ability to speak
English well, race, veteran status, employment status, and occupation
and industry. Independent variables for the Housing Data (Part 2)
include number of bedrooms, number of other rooms, building age,
whether unit was a condominium or detached single-family house,
acreage, and whether the unit had a kitchen, plumbing, public sewers,
and water service. Both files include the following variables as
separating factors: census geographic division, cost-of-living index,
percentage unemployed, percentage vacant housing, labor force employed
in manufacturing, living near a coastline, living or working in the
central city, per capita local taxes, per capita intergovernmental
revenue, per capita property taxes, population density, and commute
time to work. Lastly, the following variables measured amenities or
disamenities: average precipitation, temperature, windspeed, sunshine,
humidity, teacher-pupil ratio, number of Superfund sites, total
suspended particulate in air, and rates of murder, rape, robbery,
aggravated assault, burglary, larceny, auto theft, violent crimes, and
property crimes.

Response Rates:
Not applicable.

Presence of Common Scales:
None.

Extent of Processing: ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of
disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major
statistical software formats as well as standard codebooks to accompany the data. In addition to
these procedures, ICPSR performed the following processing steps for this data collection:

Standardized missing values.

Checked for undocumented or out-of-range codes.

Version(s)

Original ICPSR Release: 2001-10-09

Version History:

2006-01-18 File UG3161.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.

2006-01-18 File CB3161.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.

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